• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1243-1244
• /
• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.47 no.2
• /
• pp.59-75
• /
• 2019

This study was carried out to explore the 4th Industrial Revolution technology from the perspective of the landscape industry to provide the basic data necessary to increase the virtuous circle value. The 4th Industrial Revolution, the characteristics of the landscape industry and urban regeneration were considered and the methodology was established and studied including the technical classification system suitable for systematic research, which was selected as a framework. First, the 4th Industrial Revolution technology based on digital data was selected, which could be utilized to increase the value of the virtuous circle for the landscape industry. From 'Element Technology Level', and 'Core Technology' such as the Internet of Things, Cloud Computing, Big Data, Artificial Intelligence, Robot, 'Peripheral Technology', Virtual or Augmented Reality, Drones, 3D 4D Printing, and 3D Scanning were highlighted as the 4th Industrial Revolution technology. It has been shown that it is possible to increase the value of the virtuous circle when applied at the 'Trend Level', in particular to the landscape industry. The 'System Level' was analyzed as a general-purpose technology, and based on the platform, the level of element technology(computers, and smart devices) was systematically interconnected, and illuminated with the 4th Industrial Revolution technology based on digital data. The application of the 'Trend Level' specific to the landscape industry has been shown to be an effective technology for increasing the virtuous circle values. It is possible to realize all synergistic effects and implementation of the proposed method at the trend level applying the element technology level. Smart gardens, smart parks, etc. have been analyzed to the level they should pursue. It was judged that Smart City, Smart Home, Smart Farm, and Precision Agriculture, Smart Tourism, and Smart Health Care could be highly linked through the collaboration among technologies in adjacent areas at the Trend Level. Additionally, various utilization measures of related technology applied at the Trend Level were highlighted in the process of urban regeneration, public service space creation, maintenance, and public service. In other words, with the realization of ubiquitous computing, Hyper-Connectivity, Hyper-Reality, Hyper-Intelligence, and Hyper-Convergence were proposed, reflecting the basic characteristics of digital technology in the landscape industry can be achieved. It was analyzed that the landscaping industry was effectively accommodating and coordinating with the needs of new characters, education and consulting, as well as existing tasks, even when participating in urban regeneration projects. In particular, it has been shown that the overall landscapig area is effective in increasing the virtuous circle value when it systems the related technology at the trend level by linking maintenance with strategic bridgehead. This is because the industrial structure is effective in distributing data and information produced from various channels. Subsequent research, such as demonstrating the fusion of the 4th Industrial Revolution technology based on the use of digital data in creation, maintenance, and service of actual landscape space is necessary.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.10a
• /
• pp.114-116
• /
• 2016

Recently, SDN and NFV technology have been developed actively and provide enormous flexibility of network provisioning. The future network services would generally involve many different types of services such as hologram games, social network live streaming videos and cloud-computing services, which have dynamic service requirements. To provision networks for future services dynamically and efficiently, SDN/NFV orchestrators must clearly understand the service requirements. Currently, network provisioning relies heavily on QoS parameters such as bandwidth, delay, jitter and throughput, and those parameters are necessary to describe the network requirements of a service. However it is often difficult for users to understand and use them proficiently. Therefore, in order to maintain interoperability and homogeneity, it is required to have a service abstraction layer between users and orchestrators. The service abstraction layer analyzes ambiguous user's requirements for the desired services, and this layer generates corresponding refined services requirements. In this paper, we present our initial effort to design a Smart Service Abstraction Layer (SmSAL) for future network architecture, which takes advantage of machine learning method to analyze ambiguous and abstracted user-friendly input parameters and generate corresponding network parameters of the desired service for better network provisioning. As an initial proof-of-concept implementation for providing viability of the proposed idea, we implemented SmSAL with a decision tree model created by learning process with previous service requests in order to generate network parameters related to various audio and video services, and showed that the parameters are generated successfully.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.4
• /
• pp.131-142
• /
• 2018

In Korea, for the past 50 years, industrial parks have played an important role in economic growth as a cluster of national key industries. However, due to various problems of these old industrial parks, they are weakening competitiveness. It is necessary to be converted into a model for the management and fostering of high-tech industrial complex park by classifying them into development plans, management plans, and support plans according to types and characteristics of industrial parks. For this purpose, we propose CIPs (Connected-Industrial parks) using new technologies such as Cloud Computing, RFID, WSN, CPS, and Big Data analysis based on IoT. It is a hub that supports various services in transportation, warehousing and manufacturing fields while possessing and operating physical assets as concept. each CIP (Connected-Industrial park) is connected and expanded Through such CIPs, network-type collaborative manufacturing and intelligent logistics innovation enables cost reduction, delivery shortening, quality improvement.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.28 no.1
• /
• pp.9-18
• /
• 2015

In this paper, we verify the optimal topology design for heat conduction problems in steady stated which is obtained numerically using the adjoint design sensitivity analysis(DSA) method. In adjoint variable method(AVM), the already factorized system matrix is utilized to obtain the adjoint solution so that its computation cost is trivial for the sensitivity. For the topology optimization, the design variables are parameterized into normalized bulk material densities. The objective function and constraint are the thermal compliance of the structure and the allowable volume, respectively. For the experimental validation of the optimal topology design, we compare the results with those that have identical volume but designed intuitively using a thermal imaging camera. To manufacture the optimal design, we apply a simple numerical method to convert it into point cloud data and perform CAD modeling using commercial reverse engineering software. Based on the CAD model, we manufacture the optimal topology design by CNC.

• Journal of the Korean Society of Industry Convergence
• /
• v.4 no.3
• /
• pp.319-327
• /
• 2001

The objective of this study is to clarify the effect of thermal radiation environments on human thermal comfort, depending on different canyon types and surface materials on the human thermal comfort in a housing complex in Pohang city, Korea. For this purpose, the operative temperature and new effective temperature were calculated based on the modified mean radiant temperature of canyon models variated by the existence of direct radiation existence, surface materials, and the width and length of the street spaces in a housing complex. These indices for the canyon have been calculated from the meteorological data of Pohang city, which include air temperature, relative humidity, air velocity, global solar radiation and cloud. And the monthly averages of these climate factors measured at noon have been used. The results are as follows: (1) It is revealed that the short-wave radiosity reached the human body is affected by direct solar radiation and surface materials, and the long-wave radiosity by canyon types. (2) The existence of direct solar radiation, the kinds of surface materials and canyon types affect operative temperature($OT_n$) and new effective temperature($ET^*{_n}$). (3) The analysis of the human heat balance in the canyon indicates that the influence of radiation on human body is marc likely to be affected by the existence of direct solar radiation on human model.

• Journal of the Society of Naval Architects of Korea
• /
• v.55 no.1
• /
• pp.66-74
• /
• 2018

In order to evaluate the precision degree of the blocks on the dock, the shipyards recently started to use the point cloud approaches using the 3D scanners. However, they hesitate to use it due to the limited time, cost, and elaborative effects for the post-works. Although it is somewhat traditional instead, they have still used the electro-optical wave devices which have a characteristic of having less dense point set (usually 1 point per meter) around the contact section of two blocks. This paper tried to expand the usage of point sets. Our approach can estimate the rework time to weld between the Pre-Erected(PE) Block and Erected(ER) block as well as the precision of block construction. In detail, two algorithms were applied to increase the efficiency of estimation process. The first one is K-mean clustering algorithm which is used to separate only the related contact point set from others not related with welding sections. The second one is the Concave hull algorithm which also separates the inner point of the contact section used for the delayed outfitting and stiffeners section, and constructs the concave outline of contact section as the primary objects to estimate the rework time of welding. The main purpose of this paper is that the rework cost for welding is able to be obtained easily and precisely with the defective point set. The point set on the blocks' outline are challenging to get the approximated mathematical curves, owing to the lots of orthogonal parts and lack of number of point. To solve this problems we compared the Radial based function-Multi-Layer(RBF-ML) and Akima interpolation method. Collecting the proposed methods, the paper suggested the noble point matching method for minimizing the rework time of block-welding on the dock, differently the previous approach which had paid the attention of only the degree of accuracy.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.38 no.1
• /
• pp.92-102
• /
• 2020

The followings are the landscape preference aspects from the 816 landscapes(景, Kyung), which comprise the 78 modern Palkyungs, presented by the 78 local governments in Korea. First, the natural environment elements selected as Kyung(景), which are topographical landscapes, mostly consist of mountain elements such as mountains, terrace(臺), rocks and stones and water elements classified as rivers, oceans, and lakes. Natural elements also include old-growth and giant trees such as pines, ginkgos, Japanese cornels and fringe trees, tree-lined streets and forests, and plant elements such as azaleas, rhododendrons, lotuses, reeds, and silver grasses which provide seasonal landscapes. Second, more than half of Kyung, selected as human environment elements, are historical and cultural heritages such as graveyards, mountain fortresses, town fortresses, traditional villages, pavilion in villas, and temples. And it is followed by leisure tourism facilities such as traditional markets, exhibition halls, theme parks, beaches, and food streets, green-based structures such as trails, plazas, parks, and botanical gardens, and industrial heritages such as ranches, abandoned coal mines, stations, ports and bridges. Third, modern Palkyungs include objects not related to the views such as local representative facilities, regional products, and festivals. Fourth, although most of the modern Palkyungs consist of eight, some include 20, 38, or 100 in order to increase the number of objects of public relations. Fifth, a certain local government makes two modern Palkyungs with different subjects by introducing traditional Palkyung and modern Palkyung altogether. In this case, it presents several modern Palkyungs like by selecting Palkyungs in a limited area. Furthermore, one Palkyung includes numerous place names at a time in some cases. Sixth, Sosangjeonhyeong(瀟湘典型)-style modern Palkyung uses 'NakAn(落雁)' as the name of Kyung. Sosangyusahyeong(瀟湘類似型)-style modern Palkyung expresses 'Hyojong(曉鐘)' and landscape of glow of the setting sun, sunset, night view, dawn, sunrise and depicts cloud, sunset, moon, and snow. There are many Myeongsocheheomhyeong(名所體驗型)-style Palkyungs exhibiting the behavior of tourism and Myeongseunghyeong(名勝型)-style Palkyungs raising the awareness only by the names of the places. Seventh, modern Palkyung's naming styles are diverse, such as using only four letters instead of specifying Kyungmul(景物) or Kyungsaek(景色) in combination with Chinese characters or adding modifiers specializing in places.

• Korean Journal of Heritage: History & Science
• /
• v.55 no.4
• /
• pp.212-240
• /
• 2022

This study is a multi-layered exploration of 「The Thirty-Six Scenery of Seongdeok Summer Mountain Resort(承德避暑山莊三十六景)」 (The 36th view of Kangxi) recited by Emperor Kangxi of China through literature study, ancient calligraphy diagrams, and field studies. The conclusion of tracing the landscape characteristics and implications contained in 「The 36th view of Kangxi」 through the analysis of the headword(標題語) and the interpretation of the Jeyeong poem(題詠詩) is as follows. 「The 36th view of Kangxi」 is an extension of the outer edge of the Eight Sceneries, and when compared to the existing Eight Sceneries peom and Eight Sceneries painting, it is found that the landscape is centered on the 'viewpoint' rather than the landscape object. In particular, it aimed to create a structured landscape centered on nine types of buildings represented by 'Jeon(殿)' and 'Jeong(亭)' was given. In particular, Yeouiju, located in Lake district, is a scenic country endowed with the character of a gardens in Garden, which is composed by collecting famous representative Chinese landscapes and landscapes of Sansu-si and Sanshu Painting. As a result of headword analysis to understand the characteristics of landscape components, 14 landscapes (38.9%) related to water elements and 13 landscapes(36.1%) related to mountain elements, the elements related to architecture and civil engineering were classified in the order of 3 cases(8.3%), and the elements related to the skylight were classified in the order of 2 cases(5.6%). However, in Jeyeong-si, the mention of landscape vocabulary for climate elements was overwhelming. In other words, in the poems of 「The 36th Scenery of Kangxi」, scenery vocabulary symbolizing 'coolness' such as 雲(cloud), 水(water), 泉(spring), 清(clear), 波(wave), 流(wave), 風(wind) and 無暑(without heat), etc. It is not a coincidence that it appears, and it is strongly attached to the sense of place of Summer Mountain Resort in Rehe(熱河). Among the 23 landscapes whose seasonal background was confirmed, the fact that the lower landscape is portrayed as the majority and the climate elements of the resort area are portrayed in three-dimensional and multi-dimensional ways are closely related to the period of enjoying the gardens of Kangxi, the main subject of the landscape. In addition, many animal and plant landscapes appearing in Jeyeong-si appear to be in the same context as the spatial attributes of not only recreation, but also contemplation and hunting. On the other hand, in Jeyeongsi, there are 33 wonders(91.7%) citing famous people and famous books through ancient poems, old stories, and ancient stories tends to be prominent. It is inferred that this was based on Kangxi's understanding and pride in traditional Chinese culture. In 「The 36th view of Kangxi」, not only a book-writing description of the feelings of being entrusted to the family sutras, but also the spirit of patriotism, love, self-discipline and respect for mother and filial piety are strongly implied. Ultimately, 「The 36th view of Kangxi」 shows the real scene of the resort, as well as the spiritual dimension, in a multi-faceted and three-dimensional way, and the spirit of an emperor based on the dignity of the royal family and the sentiments of a writer it deserves to be called a collection of imperial records that were intended to reveal.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.64-80
• /
• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.